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This paper proposes a comprehensive hierarchical control framework forautonomous decision-making arising in robotics and autonomous systems. In atypical hierarchical control architecture, high-level decision making is oftencharacterised by discrete state and decision/control sets. However, a rationaldecision is usually affected by not only the discrete states of the autonomoussystem, but also the underlying continuous dynamics even the evolution of itsoperational environment. This paper proposes a holistic and comprehensivedesign process and framework for this type of challenging problems, from newmodelling and design problem formulation to control design and stabilityanalysis. It addresses the intricate interplay between traditional continuoussystems dynamics utilized at the low levels for control design and discreteMarkov decision processes (MDP) for facilitating high-level decision making. Wemodel the decision making system in complex environments as a hybrid systemconsisting of a controlled MDP and autonomous (i.e. uncontrolled) continuousdynamics. Consequently, the new formulation is called as hybrid Markov decisionprocess (HMDP). The design problem is formulated with a focus on ensuring bothsafety and optimality while taking into account the influence of both thediscrete and continuous state variables of different levels. With the help ofthe model predictive control (MPC) concept, a decision maker design scheme isproposed for the proposed hybrid decision making model. By carefully designingkey ingredients involved in this scheme, it is shown that the recursivefeasibility and stability of the proposed autonomous decision making scheme areguaranteed. The proposed framework is applied to develop an autonomous lanechanging system for intelligent vehicles.

A hierarchical control framework for autonomous decision-making systems: Integrating HMDP and MPC